Instance Segmentation by Learning Deep Feature in Embedding Space

The proposal-based framework is the mainstream architecture for instance segmentation. However, such architecture typically ignores the interference between objects, which fails to correctly segment overlapping objects with same category or appearance. In this paper, we propose a new instance segmentation network named Instance Discrimination Network (ID-Net) to consider the interference between objects by mapping pixels into an embedding space so that the pixels from different objects can be distinguished more accurately. To identify the foreground object in RoI, we learn a discriminative deep feature that can represent the embedding vectors corresponding to the foreground. Then, we get the foreground confidence map by calculating the similarities between the deep feature and embeddings. The experiments on PASCAL VOC and COCO datasets demonstrate the effectiveness of our method.